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import numpy as np
import triton_python_backend_utils as pb_utils


class TritonPythonModel:
    """
    Decoupled model that produces N responses based on input value.
    """

    def execute(self, requests):
        for request in requests:
            # Get input - number of responses to produce
            in_tensor = pb_utils.get_input_tensor_by_name(request, "IN")
            count = in_tensor.as_numpy()[0]

            response_sender = request.get_response_sender()

            # Produce 'count' responses, each with 0.5 as the output value
            for i in range(count):
                out_tensor = pb_utils.Tensor("OUT", np.array([0.5], dtype=np.float32))
                response = pb_utils.InferenceResponse(output_tensors=[out_tensor])
                response_sender.send(response)

            # Send final flag
            response_sender.send(flags=pb_utils.TRITONSERVER_RESPONSE_COMPLETE_FINAL)

        return None
